Indigenous-led toxic tours opening pathways for (re)connecting to place, people and all creation
Bibliographic record
Abstract
Abstract Home to nine Tribal Nations, the northeastern corner of Oklahoma (US) is a place of immense resilience, cultural beauty and attachment to place. Horrifically, however, this same area is also home to massive environmental assaults that have occurred as a result of decades of lead and zinc mining. The improperly managed mine waste that has accumulated since the late 1800s now severely contaminates the water, land and air, having adverse impacts on the health of the ecosystem and the local human community alike. Leading the fight for cleanup and support of place and people since 1997 is the non-profit organisation called Local Environmental Action Demanded (LEAD Agency). One of LEAD’s primary tools for education and advocacy has been leading toxic tours across these harmed lands and waters. This contribution draws upon the nearly three decades of toxic tours that Rebecca and Earl have led by sharing key stories and experiences of important sites visited along the way, offering a snapshot of toxic tour experience. Drawing on Indigenous storywork and autoethnographic methodologies, this contribution aims to spotlight the potential of Indigenous-led toxic tours for helping to (re)connect people — both locals and visitors — to place and a responsibility of stewardship.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".